report.Rmd

---
title: "REPORT"
output:
  html_document:
    toc: true
    toc_float: true
  pdf_document:
    toc: true
params:
  gVars: NA
  input: NA
  logVars: NA
  srcDir: NA
---

# R version and platform
```{r, , echo=FALSE}
base::version
```

# Timestamp
```{r, , echo=FALSE}
print(Sys.time())
```

# Analysis mode
```{r, , echo=FALSE}
print(paste("Is the analysis run with a GLP mode? ", params$input$mode == "TRUE", sep = ""))
```

# Metadata parameters
```{r, , echo=FALSE}
width_p = 900

cat(paste("Time point columns: ", params$logVars$phenoTPVar,"\n",
            "Dose columns: ", params$logVars$phenoDoseVar, "\n", 
            "Optional variables: ",params$logVars$phenoOptVar,sep = ""))
```

# Metadata description
```{r, , echo=FALSE}
if(!is.null(params$gVars$pheno_data_pie_chart_description)){
  p = params$gVars$pheno_data_pie_chart_description
  ggplotly(p,width = width_p)
}

if(!is.null(params$gVars$rendered_pheno_table))
DT::datatable(gVars$rendered_pheno_table, filter="top",
              selection = 'single',
              options = list(
                search = list(regex=TRUE, caseInsensitive=FALSE),
                scrollX=TRUE,
                ordering=T, 
                rownames = FALSE
              ))
```

# Expression data 
```{r, , echo=FALSE}

if(!is.null(params$input$x_ID) & !is.null(params$input$y_ID)){
  cat(paste("X Variable Name: ", params$input$x_ID,"\n",
            "Y Variable Name: ", params$input$y_ID, "\n",sep = ""))
}

```

## Expression data 

```{r, , echo=FALSE} 
cat(paste("Mds plot for experiment: ",params$input$experiment_for_data, "\n", sep = "" ))
if(!is.null(params$gVars$expression_mds)){
  p = params$gVars$expression_mds
  ggplotly(p,width = width_p)
}

```


# Filtering analysis
```{r, , echo=FALSE}
cat(paste("Was anova performed? ", params$gVars$was_anova_performed,"\n",
          "Was trend test performed? ", params$gVars$was_trend_test_performed, "\n",
          "Was differential analysis performed? ", params$gVars$was_logFC_filtering_performed, "\n", 
          "The finald filtering performd was: ", params$gVars$final_filtering_strategy, sep = ""))

if(!is.null(params$gVars$final_filtering_strategy)){
  #"anova" "trend test" "differential analysis"
if(params$gVars$final_filtering_strategy == "anova"){
  
  Pval.th=as.numeric(params$input$anovaPvalTh)
  Adjustment=params$input$anovaPvalAdj
  Cores=as.numeric(params$input$anovaNCores)
  
 cat(paste("Anova parameters", "\n",
          "p.value adjustment method: ", Adjustment,"\n",
          "p.value threshold: ", Pval.th, "\n",
          "number of cores: ", Cores, sep = ""))
}

if(params$gVars$final_filtering_strategy == "trent test"){
    Pval.th=as.numeric(params$input$trendPvalTh)
    Adjustment=params$input$TrendPvalAdj
    Cores=as.numeric(params$input$trendNCores)
    
    cat(paste("Trend test parameters", "\n",
            "p.value adjustment method: ", Adjustment,"\n",
            "p.value threshold: ", Pval.th, "\n",
            "number of cores: ", Cores, sep = ""))
}

if(params$gVars$final_filtering_strategy == "differential analysis"){
  
    cat(paste("Differential analysis parameters", "\n",
          "p.value adjustment method: ", params$input$fcPvalAdj,"\n",
          "p.value threshold: ", as.numeric(params$input$fcPvalTh), "\n",
          "number of cores: ", as.numeric(params$input$fcNCores), sep = ""))
}

 DT::datatable(params$gVars$Anova_tab, filter="top",
                options = list(
                  search = list(regex=TRUE, caseInsensitive=FALSE), #MAYBE remove search bars
                  scrollX=TRUE,
                  ordering=T
                )
  )
}

if(!is.null(gVars$anova_pie)){
  print(gVars$anova_pie)
}
          

```


# BMD Analysis setting
```{r, , echo=FALSE}
  cat(paste("Number of cores: ", params$input$BMDNCores, "\n",
            "Maximum number of iterations for model fitting: ", params$input$BMDMaxIter,"\n",
            "Data type: ", params$input$BMDdataType,"\n",
            "Deviation type: ", params$input$BMDdeviationType,"\n",
            "Variance type: ", params$input$variance_type,"\n",
            "Response level: ", params$input$RespLev,"\n",
            "Assumption of constant variance: ", params$input$constantVar,"\n",
            "Use model variance when fitting the models: ", params$input$BMDmodelVar, "\n",
            "Threshold for levene test: ", params$input$BMDsignificance, "\n",
            "Confidence interval: ", params$input$conf_interval,"\n",
            "Threshold on lack-of-fit: ", params$input$LOOF,"\n",
            "Model selection settings:", params$input$BMDSettings, "\n",
            paste("Fitted models: ",params$input$ModGroup, collapse = "\n"),sep = ""))

```

## Render BMD original results
```{r, , echo=FALSE}
mat = params$gVars$all_models_statistics
if(!is.null(mat))
DT::datatable(mat, filter="top",
                  selection = 'single',
                  options = list(
                    search = list(regex=TRUE, caseInsensitive=FALSE), #MAYBE remove search bars
                    scrollX=TRUE,
                    ordering=T))
```


## BMD Filtering
```{r, , echo=FALSE}
print(paste("Where filter on BMD values applied? ", gVars$is_bmd_filtering_performed, sep = ""))

if(gVars$is_bmd_filtering_performed == TRUE){
  
  cat(paste("Number of cores for the BMD filtering: ", params$input$BMDN_filter_Cores, "\n",
            "Filter by NA values: ", params$input$filter_bmdl_na,"\n",
            "Filter by Lack of Fit: ", params$input$filter_bmdl_loof,"\n",
            "Threshold for the Lack of Fit Pvalue: ", params$input$bmdloofth,"\n",
            "Apply Ratio filters: ", params$input$ratio_filter2,"\n",
            "BMD/BMDL ratio: ", params$input$bmd_bmdl_th2,"\n",
            "BMDU/BMD ratio: ", params$input$bmdu_bmd_th2,"\n",
            "BMDU/BMDL ratio: ", params$input$bmdu_bmdl_th2,"\n",
            "Filter by Lower Boundary: ", params$input$filter_bounds_bmdl_bmdu2_low,"\n",
            "Lowest dose filter: ", params$input$min_dose_perc2,"\n",
            "Filter by Upper Boundary: ", params$input$filter_bounds_bmdl_bmdu2_high,"\n",
            "Highest dose filter: ", params$input$max_dose_perc2,"\n",
            "Filter models with R2<th: ", params$input$filter_by_R2,"\n",
            "Threshold R2: ", params$input$BMD_filter_r2,"\n",
            "filter_by_monotonicity: ", params$input$BMD_filter_r2,
            sep = ""))
}
```

## Select optimal model
```{r, , echo=FALSE}
  print(paste("Is optimal model selection performed? ", params$gVars$is_optimal_model_selected, sep = ""))
  if(params$gVars$is_optimal_model_selected == TRUE)
  print(paste("Method to select the optimal model: ", params$input$select_best_model_options, sep = ""))
```

## Compute Model Average
```{r, , echo=FALSE}
  print(paste("Model average? ", params$gVars$is_average_computed, sep = ""))
```

## Render BMD final results, after filtering, and/or model average computation, and/or best model selection
```{r, , echo=FALSE}
mat = params$gVars$bmdlatestTable
if(!is.null(mat))
DT::datatable(mat, filter="top",
                  selection = 'single',
                  options = list(
                    search = list(regex=TRUE, caseInsensitive=FALSE), #MAYBE remove search bars
                    scrollX=TRUE,
                    ordering=T))
```


# Comparison between time points

## Number of genes by time
```{r, , echo=FALSE}

if(!is.null(params$gVars$NGTime)){
  cat(paste("Group by: ", params$input$tp_groupby1_input, "\n",
            "Filter by: ", params$input$tp_filtercol_input, "\n", 
            "Filter values: ", params$input$tp_filterby_input, sep = ""))

p = params$gVars$NGTime
if(!is.null(p)) ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```



## BMD Distribution
```{r, , echo=FALSE}

if(!is.null(params$gVars$BMD_dist_TP)){
  cat(paste("Color by: ", params$input$BMDVals_colorby_input,"\n",
          "Group by: ", params$input$BMDVals_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDVals_groupby2_input, "\n",
          "Filter by: ", params$input$BMDVals_filtercol_input, "\n",
          "Filter values: ", params$input$BMDVals_filterby_input,
          sep = ""))

p = params$gVars$BMD_dist_TP
if(!is.null(p)) ggplotly(p,width = width_p)

}else{
  print("Not performed")
}

```

## BMDL Distribution
```{r, , echo=FALSE}

if(!is.null(params$gVars$BMDL_dist_TP)){
  cat(paste("Color by: ", params$input$BMDVals_colorby_input,"\n",
          "Group by: ", params$input$BMDVals_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDVals_groupby2_input, "\n",
          "Filter by: ", params$input$BMDVals_filtercol_input, "\n",
          "Filter values: ", params$input$BMDVals_filterby_input,
          sep = ""))


p = params$gVars$BMDL_dist_TP
if(!is.null(p)) ggplotly(p,width = width_p)

}else{
  print("Not performed")
}

```

## BMDU Distribution
```{r, , echo=FALSE}

if(!is.null(params$gVars$BMDU_dist_TP)){
  cat(paste("Color by: ", params$input$BMDVals_colorby_input,"\n",
          "Group by: ", params$input$BMDVals_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDVals_groupby2_input, "\n",
          "Filter by: ", params$input$BMDVals_filtercol_input, "\n",
          "Filter values: ", params$input$BMDVals_filterby_input,
          sep = ""))


p = params$gVars$BMDU_dist_TP
if(!is.null(p)) ggplotly(p,width = width_p)

}else{
  print("Not performed")
}

```

## BMDR Distribution
```{r, , echo=FALSE}

if(!is.null(params$gVars$BMR_dist_TP)){
  cat(paste("Color by: ", params$input$BMDVals_colorby_input,"\n",
          "Group by: ", params$input$BMDVals_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDVals_groupby2_input, "\n",
          "Filter by: ", params$input$BMDVals_filtercol_input, "\n",
          "Filter values: ", params$input$BMDVals_filterby_input,
          sep = ""))

p = params$gVars$BMR_dist_TP
if(!is.null(p)) ggplotly(p,width = width_p)

}else{
  print("Not performed")
}

```


## IC50 Distribution
```{r, , echo=FALSE}

if(!is.null(params$gVars$IC50_dist_TP)){
  cat(paste("Color by: ", params$input$BMDVals_colorby_input,"\n",
          "Group by: ", params$input$BMDVals_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDVals_groupby2_input, "\n",
          "Filter by: ", params$input$BMDVals_filtercol_input, "\n",
          "Filter values: ", params$input$BMDVals_filterby_input,
          sep = ""))

p = params$gVars$IC50_dist_TP
if(!is.null(p)) ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```

## BMD R2 fitting
```{r, , echo=FALSE}
if(!is.null(params$gVars$BMD_r2_fitting)){
  cat(paste("Color by: ", params$input$LOOF_colorby_input,"\n",
          "Group by: ", params$input$LOOF_groupby1_input, "\n",
          "Group2 by: ", params$input$LOOF_groupby2_input, "\n",
          "Filter by: ", params$input$LOOF_filtercol_input, "\n",
          "Filter values: ", params$input$LOOF_filterby_input,
          sep = ""))


p = params$gVars$BMD_r2_fitting
if(!is.null(p))
ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```


## BMD AIC fitting
```{r, , echo=FALSE}
if(!is.null(params$gVars$BMD_aic_fitting)){
  cat(paste("Color by: ", params$input$LOOF_colorby_input,"\n",
          "Group by: ", params$input$LOOF_groupby1_input, "\n",
          "Group2 by: ", params$input$LOOF_groupby2_input, "\n",
          "Filter by: ", params$input$LOOF_filtercol_input, "\n",
          "Filter values: ", params$input$LOOF_filterby_input,
          sep = ""))

p = params$gVars$BMD_aic_fitting
if(!is.null(p))
ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```


## BMD p-value fitting
```{r, , echo=FALSE}
if(!is.null(params$gVars$BMD_pval_fitting)){
  cat(paste("Color by: ", params$input$LOOF_colorby_input,"\n",
          "Group by: ", params$input$LOOF_groupby1_input, "\n",
          "Group2 by: ", params$input$LOOF_groupby2_input, "\n",
          "Filter by: ", params$input$LOOF_filtercol_input, "\n",
          "Filter values: ", params$input$LOOF_filterby_input,
          sep = ""))

p = params$gVars$BMD_pval_fitting
if(!is.null(p))
ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```

## BMD/BMLDL
```{r, , echo=FALSE}
if(!is.null(params$gVars$BMD_BMDL)){
  cat(paste("Color by: ", params$input$BMDBMDL_colorby_input,"\n",
          "Group by: ", params$input$BMDBMDL_groupby1_input, "\n",
          "Group2 by: ", params$input$BMDBMDL_groupby2_input, "\n",
          "Filter by: ", params$input$BMDBMDL_filtercol_input, "\n",
          "Filter values: ", params$input$BMDBMDL_filterby_input,
          sep = ""))

p = params$gVars$BMD_BMDL
if(!is.null(p))
ggplotly(p,width = width_p)
}else{
  print("Not performed")
}

```

## Fitted models
```{r, echo=FALSE}
if(!is.null(params$gVars$BMD_dist_models)){
  cat(paste("Group by: ", params$input$Fittedmodels_groupby1_input,"\n",
          "Group2 by: ", params$input$Fittedmodels_groupby2_input, 
          "Filter by: ", params$input$Fittedmodels_filtercol_input,
          "Filter values: ", params$input$Fittedmodels_filterby_input,
          sep = ""))

p = params$gVars$BMD_dist_models
if(!is.null(p)) plot(p)
}else{
  print("Not performed")
}

```


## Compare experiments cumulative plot
```{r, echo=FALSE}
if(!is.null(gVars$bmdcompareexpRes_ecdf_plot)){
  cat(paste("Variable of interest: ", params$input$BMDComExp_relvar_input, "\n",
          "Group by: ", params$input$BMDComExp_groupby2_input,"\n",
          "Other variables: ", params$input$BMDComExp_othervars_input,"\n",
          "Filter by: ", params$input$compare_experiments_filtercol_input, "\n", 
          "Filter values: ", params$input$compare_experiments_filterby_input, sep = ""))


ggplotly(params$gVars$bmdcompareexpRes_ecdf_plot)
}else{
  print("Not performed")
}

```

## Compare experiments upset plot
```{r, echo = FALSE}

if(!is.null(params$gVars$bmdcompareexpRes_upset_plot)){
  cat(paste("Maximum number of intersection: ", params$input$maxInt, "\n",
          "Group by: ", params$input$groupby,"\n",
          "Order by: ", params$input$orderby,"\n", sep = ""))


params$gVars$bmdcompareexpRes_upset_plot
}else{
  print("Not performed")
}


```

# Gene frequency
```{r, echo=FALSE}
cat(paste(
      "Experiment id: ",params$input$select_exp_id, "\n",
      "Group by: ", params$input$gene_frequency_groupby_input,"\n",
      "Order by: ", params$input$gene_frequency_splitby_input,"\n", sep = ""))

DT::datatable(gVars$gene_freq_m_list[[params$input$select_exp_id]])
```

```{r, echo=FALSE}

if(!is.null(gVars$gene_freq_lineplot_plot_list[[params$input$select_exp_id]])){
  ggplotly(gVars$gene_freq_lineplot_plot_list[[params$input$select_exp_id]])
}else{
  print("Not performed")
}
```

# FunMappONE
```{r, echo=FALSE}
if(!is.null(gVars$EnrichDatList)){
  
cat(paste("---- ENRICHMENT PARAMETERS ----\n",
          "Organisms: ", params$input$organism, "\n",
          "GeneID: ", params$input$idtype, "\n",
          "Select Functional Annotation: ", params$input$EnrichType, "\n",
          "Select GO: ", params$input$GOType, "\n",
          "Correction Method: ", params$input$pcorrection, "\n",
          "P-value threshold: ", params$input$pvalueTh, "\n",
          "Significant Results only: ", params$input$only_significant, "\n",
          "Annotated genes only: ", params$input$only_annotated, "\n",
          "Minimum number of genes in the intersection: ", params$input$min_intersection, "\n",
          "Aggregation Function: ", params$input$aggregation, "\n",
          "Plot modification: ", params$input$continuous, "\n",
          "Choose Values Type: ", params$input$MapValueType, "\n",
          sep = "")
)
  
} else {
  print("No enrichment performed were provided.")
}
```

## Heatmap
```{r, echo=FALSE, fig.width=10, fig.height=8}
if(!is.null(gVars$EnrichDatList) & !is.null(gVars$toPlot)){
  print(ggplotify::as.ggplot(gVars$toPlot))
}else {
  print("No heatmap computed.")
}
```

## Range plot
```{r, echo=FALSE, fig.width=10, fig.height=8}
if(!is.null(gVars$EnrichDatList) & !is.null(gVars$funmappone_range_plot)){
    ggplotly(gVars$funmappone_range_plot)
}else {
  print("No range plot computed.")
}


```

#KE enrichment analysis
## Range plot
```{r, echo=FALSE, fig.width=10, fig.height=8}

cat(paste("---- KE & AOP ENRICHMENT PARAMETERS ----\n",
          "Timestamp: ", Sys.time(), "\n",
          
          # KE enrichment
          "KE: Significant p-value threshold: ", params$input$ke_sig_pval, "\n",
          "KE: Correction method: ", params$input$ke_correction_method, "\n",
          "KE: Significant Results only: ", params$input$ke_only_significant, "\n",
          
          # AOP enrichment
          "AOP: Significant p-value threshold: ", params$input$aop_sig_pval, "\n",
          "AOP: Correction method: ", params$input$aop_correction_method, "\n",
          "AOP: Significant Results only: ", params$input$aop_only_significant, "\n",
          
          # Shared / advanced parameters
          "Minimum AOP length for fingerprint: ", params$input$min_aop_length_fingerprint, "\n",
          "Percentage enriched KE: ", params$input$percentage_enriched_ke, "\n",
          "Aggregation function: ", params$input$KE_aggregation_function, "\n",
          
          # Background option
          "User-provided background: ", ifelse(params$input$user_background, "TRUE", "FALSE"), "\n",
          "User-provided background file: ", ifelse(params$input$user_background, params$input$background$name, ""), "\n",
          
          sep = "")
)
```

# KE enrichment table
```{r, echo=FALSE}
if(!is.null(gVars$KE_enrichment_res)){
    toPlotData = gVars$KE_enrichment_res
DT::datatable(toPlotData,
                              filter  =  "top",
                              selection  =  'single',
                              options  =  list(search  =  list(regex  =  TRUE, caseInsensitive  =  FALSE), #MAYBE remove search bars
                                               scrollX  =  TRUE,
                                               ordering  =  T,
                                               pageLength = 5))

}else {
  print("No KE enrichment.")
}
```

```{r, echo=FALSE}
if(!is.null(gVars$plot_bmd_of_the_genes_in_a_ke)){
    ggplotly(gVars$plot_bmd_of_the_genes_in_a_ke)
}else{
  print("No data to plot")
}
```

##KEKE Network 
```{r, echo=FALSE}
cat(paste("---- KE / AOP NETWORK PARAMETERS ----\n",
          
          # Validation-related parameters
          "KE filtering by AOP fingerprint: ", params$input$KEr_filter_ke_by_aop_fingerprint, "\n",
          "Enlarge KE selection: ", params$input$KEr_enlarge_ke_selection, "\n",
          "Experiment selected: ", params$input$experiment_KEr, "\n",
          #"Time point selected: ", params$input$time_point_KEr, "\n",   # uncomment if used
          
          # Organism / gene mapping
          "KE organism: ", params$input$ke_organism, "\n",
          "KE gene identifier type: ", params$input$ke_gene_identifier, "\n",
          "Convert to gene symbols: ", params$input$convert_to_gene_symbol, "\n",
          
          # Network parameters
          "Maximum path length: ", params$input$max_path_length, "\n",
          "Number of AOs: ", params$input$n_AOs, "\n",
          "Number of MIEs: ", params$input$n_MIEs, "\n",
          
          # Grouping (if used)
          "Group by (ssbd): ", params$input$KEr_group_by_ssbd, "\n",
          
          sep = "")
)
```

```{r, echo=FALSE}

if(!is.null(gVars$KEr_net_list)){
  
experiment = params$input$experiment_KEr
selected_time = params$input$time_point_KEr
netList = gVars$KEr_net_list
vn = netList[[paste(experiment, selected_time, sep = "_")]]

print(experiment)
print(selected_time)
print(class(vn))

vn = vn %>%
  visPhysics(enabled = TRUE) %>%
visEvents(doubleClick = "
      function(params) {
if(params.nodes.length > 0){
  var nodeId = params.nodes[0];
  var node = this.body.data.nodes.get(nodeId); // get full node data
  node.physics = false; // change only physics
  this.body.data.nodes.update(node); // update with full data
}
      }")

vn
}else{
  print("No network displayed")
}

```

## Molecular information on top of AOPs
```{r, echo=FALSE}
# small formatter for clean output
fmt <- function(x) {
  if (is.null(x)) return("—")
  if (isTRUE(x))  return("TRUE")
  if (identical(x, FALSE)) return("FALSE")
  as.character(x)
}

cat(paste(
  "---- AOP VISNETWORK: INPUTS ----\n",
  "AOP ID (AOP_visnetwork): ",                 fmt(params$input$AOP_visnetwork), "\n",
  "Experiment (experiment_selection_AOP_visplot): ", fmt(params$input$experiment_selection_AOP_visplot), "\n",
  "Time point (time_point_selection_AOP_visplot): ", fmt(params$input$time_point_selection_AOP_visplot), "\n",
  "Only direct relations (AOP_visnet_only_direct): ", fmt(params$input$AOP_visnet_only_direct), "\n",
  "KE organism (ke_organism): ",               fmt(params$input$ke_organism), "\n",
  "KE gene identifier (ke_gene_identifier): ", fmt(params$input$ke_gene_identifier), "\n",
  sep = "")
)
```

```{r}

if(!is.null(gVars$KE_and_TF_network_for_AOP)){
  vn = gVars$KE_and_TF_network_for_AOP
  vn
}else{
  print("No network computed")
}
```

## Key players in AOP along with their PPI interactions
```{r, echo=FALSE}
# formatter for clean text
fmt <- function(x) {
  if (is.null(x)) return("—")
  if (isTRUE(x))  return("TRUE")
  if (identical(x, FALSE)) return("FALSE")
  as.character(x)
}

cat(paste(
  "---- MOLECULAR INFORMATION ON TOP OF AOPs: INPUTS ----\n",
  "AOP ID (AOP_visnetwork): ",                 fmt(params$input$AOP_visnetwork), "\n",
  "Experiment (experiment_selection_AOP_visplot): ", fmt(params$input$experiment_selection_AOP_visplot), "\n",
  "Time point (time_point_selection_AOP_visplot): ", fmt(params$input$time_point_selection_AOP_visplot), "\n",
  "Only direct relations (AOP_visnet_only_direct): ", fmt(params$input$AOP_visnet_only_direct), "\n",
  "KE organism (ke_organism): ",               fmt(params$input$ke_organism), "\n",
  "KE gene identifier (ke_gene_identifier): ", fmt(params$input$ke_gene_identifier), "\n",
  sep = "")
)
```

```{r, echo=FALSE}

if(!is.null(gVars$AOP_ppi_key_players)){
  vn = gVars$AOP_ppi_key_players
  vn
}else{
  print("No network computed")
}
```

# Gene pairs computation
```{r, echo=FALSE}
fmt <- function(x) {
  if (is.null(x)) return("—")
  if (isTRUE(x))  return("TRUE")
  if (identical(x, FALSE)) return("FALSE")
  as.character(x)
}

cat(paste(
  "---- GENE PAIR COMPARISON: INPUTS ----\n",
  "Organism (gene_comparison_organism): ", 
      fmt(params$input$gene_comparison_organism), "\n",
  "Experiment filter (Experiment_gene_comparison_filter_value): ", 
      fmt(params$input$Experiment_gene_comparison_filter_value), "\n",
  "Time filter (Time_gene_comparison_filter_value): ", 
      fmt(params$input$Time_gene_comparison_filter_value), "\n",
  sep = "")
)

```

## Gene pairs table
```{r, echo=FALSE}
if(!is.null(gVars$edge_presence_ppi)){
  edge_presence_ppi = gVars$edge_presence_ppi
 DT::datatable(edge_presence_ppi, filter="top",
                      rownames = F, selection = 'single',
                      options = list(
                        search = list(regex=TRUE, caseInsensitive=FALSE),
                        scrollX=TRUE,
                        ordering=T))
  
}else{
  print("No gene pairs computed")
}
 
```

```{r, echo=FALSE}

if(!is.null(gVars$gene_pairs_print_models)){
  p = gVars$gene_pairs_print_models
 ggplotly(p)
  
}else{
  print("No gene pairs computed")
}

```